Geometric Machine Learning

SmartUQ's Varying Geometry Emulation: Advanced Geometric Machine Learning

SmartUQ's varying geometry emulation tools provide powerful capabilities for geometric machine learning, especially when dealing with intricate, irregular, or dynamic geometries. By moving beyond the restrictions of traditional fixed-topology and taking advantage of leading Gaussian process modeling technologies, SmartUQ opens the door to fast, low-compute, data-efficient geometric machine learning.

Key Advantages and Capabilities

• Handles Irregular, Non-Parametric, Variable Geometries:
  • Unlike typical deep-learning frameworks that demand extensive preprocessing or fixed-topology meshes, SmartUQ's varying geometry emulation uses advanced kernel-based methods, specifically Gaussian processes.
  • Directly builds surrogate models from irregular and variable datasets.
  • Uniquely capable of managing significant variations in geometry, topology, or spatial coordinates between simulations or experimental runs.
• Direct Modeling from Point Cloud Data:
  • Employs fully nonparametric methods to construct predictive models directly from point cloud data.
  • Highly relevant for unstructured datasets, such as those from Lidar scans, experimental measurement points, or adaptive mesh simulations.
  • Reduces preprocessing effort, maintains original data integrity, and captures nuanced spatial relationships without the need for parameterized geometries or human feature identification .
• Efficient Bayesian Optimization:
  • Utilizes Gaussian process-based Bayesian optimization methodologies.
  • Enables efficient navigation of complex design spaces, requiring fewer simulations or experimental runs compared to traditional neural networks and deep learning.
  • Results in significant cost and time savings, especially beneficial in computationally intensive engineering applications.

A Robust Alternative to Neural Network based Geometric Deep Learning

SmartUQ’s varying geometry emulation technology offers a robust, adaptable, and computationally efficient alternative to conventional geometric deep learning. Its kernel-based Gaussian process approach easily handles changing geometries and irregular datasets with fewer data points and normal workstations, making it ideal for challenging engineering tasks that demand precise modeling from diverse and dynamic geometric data sources.